Toward a Global Public Repository of Community Protocols to Encourage Best Practices in Biomolecular Ocean Observing and Research
Bibliographic record
Abstract
Biomolecular ocean observing and research is a rapidly evolving field that uses omics approaches to describe biodiversity at its foundational level, giving insight into the structure and function of marine ecosystems over time and space. It is an especially effective approach for investigating the marine microbiome. To mature marine microbiome research and operations within a global ocean biomolecular observing network (OBON) for the UN Decade of Ocean Science for Sustainable Development and beyond, research groups will need a system to effectively share, discover, and compare “omic” practices and protocols. While numerous informatic tools and standards exist, there is currently no global, publicly-supported platform specifically designed for sharing marine omics [or any omics] protocols across the entire value-chain from initiating a study to the publication and use of its results. Toward that goal, we propose the development of the Minimum Information for an Omic Protocol (MIOP), a community-developed guide of curated, standardized metadata tags and categories that will orient protocols in the value-chain for the facilitated, structured, and user-driven discovery of suitable protocol suites on the Ocean Best Practices System. Users can annotate their protocols with these tags, or use them as search criteria to find appropriate protocols. Implementing such a curated repository is an essential step toward establishing best practices. Sharing protocols and encouraging comparisons through this repository will be the first steps toward designing a decision tree to guide users to community endorsed best practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.357 | 0.487 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.044 | 0.033 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.032 | 0.058 |
| Open science | 0.023 | 0.046 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.015 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".